1. Technical Field of the Invention
The present invention relates to a process for correcting atmospheric influences in multispectral optical remote sensing data that are acquired in different types of satellite or airborne sensors for earth observation with different geometric and/or spectral resolutions, and read in and precessed as raw data to generate an image.
2. Prior Art
A fundamental prerequisite for deriving quantitative parameters and indicators from remotely sensed data, apart from geo-referencing, is the atmospheric correction.
A number of basic processes for the atmospheric correction of multispectral remote sensing data already exists. However, some of these processes are only on a scientific level of development (laboratory samples, e.g., H. Rahman, G. Dedieu: xe2x80x9cSMACxe2x80x9d, A simplified method for atmospheric correction of satellite measurements in the solar spectrum, Int. J. Rem. Sens., 15, 1, pages 123-143, 1994, and xe2x80x9cEXACTxe2x80x9d, Th. Popp: Correcting atmospheric masking to retrieve the spectral albedo of land surfaces from satellite measurements, Int. J. Rem. Sens., 16, pages 3483-3508, 1995), which does not permit a routine automatic processing of multispectral remote sensing data from a great variety of sensors.
Then there are processes that are currently being used as industrial samples in commercial program packages for the atmospheric correction of multispectral remote sensing data, one of which is known from DE 41 02 579 C2. They are based on the determination of reference areas of a low reflectance, which must be identified in the remotely sensed data. The criteria that are used for these reference areas may be the grey value, the color, or the multispectral signature. The calculation of the atmospheric correction furthermore requires that the properties of the atmosphere in need of correction be known. As a rule, this is done by entering pre-set standard atmospheres. These processes, which are being used as industrial samples, require the interactive interaction, for example for the selection of reference areas and atmospheric parameters, by an expert who must possess specialized knowledge and experience in the field of atmospheric correction. These processes, therefore, cannot be used for an automatic atmospheric correction of remotely sensed data.
In the commercially applied industrial sample processes, the atmospheric correction is performed manually through interactive parameter adjustments and, as a rule, this is done using predefined standard information, e.g., in the form of a limited number of standard atmospheres and/or a predefined visibility. Selecting the best-suited standard information for the given remotely sensed data set being processed requires expert knowledge on the part of the operating personnel. Furthermore, until now there is no automatic identification of validation areas, e.g., of reference areas of a low reflectance and of areas of known reflectance behavior. These areas are currently also identified and marked interactively by the operating personnel.
The aforementioned scientific processes in the form of known laboratory samples are generally optimized with respect to a specific sensor or even to a specific application, or they utilize only supplemental data that are poorly correlated with respect to time/space, e.g., climatologies and weather analysis data.
Furthermore, in the known industrial samples and most laboratory samples, the anisotropy of the reflectance on the ground is not taken into consideration. A further, hitherto unsolved problem in the processing of remotely sensed data lies in the fact that, while it is true that measurements of the current atmospheric condition can be incorporated for the correction of individual data sets, as a rule, a large-scale incorporation of current atmospheric parameters can not take place within these processes.
The influence of the non-inclusion of the atmospheric parameters can be demonstrated, for example, for the so-called normalized differential vegetation index (NDVI). The NDVI is obtained from bi-spectral measurements in the red (channel 1) and in the near-infrared (channel 2) and represents a standard value which, because of the method by which it is calculated, already provides a correction of the zeroth order of the atmospheric influence. The following table provides an overview of the possible influence of the most important atmospheric parameters (ozone, water vapor, molecule or Rayleigh scattering, aerosol scattering) on data of the spectral reflectance and the NDVI based on the example of a known sensor (NOAA-AVHRR) and thus demonstrates the errors that can still be attached to this correction of the zeroth order if current atmospheric parameters are not used for the atmospheric correction. The proportional effects (transmission) are listed in the table in percentages and other information in absolute reflectances.
The present invention is based on the aim of creating a process for correcting atmospheric influences for multispectral remote sensing data that is suitable for integration into an automatic processing chain and, in contrast to processes of the prior art, therefore meets important criteria in such a way that current reference areas are determined automatically and current atmospheric parameters are used, that no interactive involvement of the operating personnel must be required, and that no expert knowledge should be required on the part of the operating personnel.
In accordance with the invention, which relates to a process of the above type, this aim is met in such a way that in a first partial process, a pre-classification of the raw data takes place for an automatic recognition of predefined classes, that, in a second partial process, a correction calculation is performed to convert the uncorrected reflectances into corrected reflectances on the ground, and that current and essentially complete supplementary data on the current atmospheric conditions are incorporated. The pre-classification permits a more precise correction calculation by generating required a priori knowledge.
Pre-tabulated/parameterized radiative transfer calculations make the inventive process fast and, therefore, suitable for operational applications. To attain a good time-space correlation of the atmospheric data with the data in need of correction, these values are estimated, as far as possible, from the data in need of correction. Additional supplementary data that cannot be obtained from the data in need of correction can be acquired externally from operational processing chains via an external interface, and interpolated with suitable methods.
In the numerical process for an automatic atmosphere correction according to the invention, the data from different sensors with different geometric and/or spectral resolution may be read in and processed as raw data, e.g, NOAA-AVHRR, ERS-ATSR, (SEA)WIFS, EOS-MODIS, Landsat-TM and Landsat-MSS, IRS-LISS, SPOT-HRV. Essential in the inventive process is the combination of an event-controlled classification and object identification, i.e., a localization and content-based correlation of objects, the actual correction calculation, and the use of current and complete supplementary data regarding the atmospheric condition. Only with this combination can an automatic atmospheric correction take place without interactive intervention or expert knowledge.
The inventive process is, therefore, composed of two partial processes, which can be advantageously joined as main modules. The first main module is used for the detection and identification of dark areas and areas of significant spectral behavior in the remotely sensed data, and the second main module is used for the atmospheric correction of the remotely sensed data.
The two main modules advantageously consist of sub-modules. These are advantageously supplemented by a database in which basic static and dynamic data, as well as a priori knowledge, e.g., spectral signatures, sensor specifications, statistical properties, correction methods and assimilation methods are stored. This database is accessible by both main modules.
The first main module for its part advantageously consists of two sub-modules. The first of these two sub-modules is used for the identification of reference areas of low reflectance, e.g., of water surfaces and dark forest areas, as well as for the identification of exclusion areas, e.g., clouds and cloud shadows. In the process, this sub-module uses the model spectra and sensor-specific information stored in the database. The second one of these two sub-modules is used to perform the homogeneity analysis for identified reference areas (test for representativeness of the selected areas) and the area size analysis, on one hand, and the analysis of the direct and indirect neighbourhood on the other hand.
The second main module, which is thus used for the atmospheric correction of the remotely sensed data, for its part advantageously also consists of two sub-modules. The first one of these two sub-modules is used for processing the required supplementary data. In the process, this sub-module accesses internal supplementary data, as they are derived from the raw data and the results from the first main module, and processes external supplementary data,which are made available via an external interface, e.g., online or via CD-ROM. This may be done using standard data assimilation and interpolation methods, which are made available in the methods database. The second one of these two sub-modules performs the actual correction steps with the aid of the supplementary data from the first sub-module. In the process, correction methods may be accessed that are stored in the methods database.